CN113555906B - Wind-fire coupling power generation system robust capacity planning method considering distribution network reconfiguration - Google Patents

Wind-fire coupling power generation system robust capacity planning method considering distribution network reconfiguration Download PDF

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CN113555906B
CN113555906B CN202110394805.9A CN202110394805A CN113555906B CN 113555906 B CN113555906 B CN 113555906B CN 202110394805 A CN202110394805 A CN 202110394805A CN 113555906 B CN113555906 B CN 113555906B
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王强钢
邹尧
胡博
周桂平
赵苑竹
周念成
杨龙杰
吴雪翚
林天皓
王顺江
王磊
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Chongqing University
State Grid Liaoning Electric Power Co Ltd
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State Grid Liaoning Electric Power Co Ltd
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Abstract

The invention discloses a robust capacity planning method for a wind-fire coupling power generation system considering distribution network reconfiguration, which comprises the following steps of: 1. linear active power balance is deduced from the actual high voltage distribution network and radial structural constraints in the distribution network are summarized. 2. And constructing a capacity planning model of the deterministic wind-fire coupling power generation system into a mixed integer linear planning model. 3. Based on an oval uncertainty set, a wind-fire coupling power generation system robust capacity planning model is provided to solve uncertainty of wind power and load change. 4. The model is reconstructed as a mixed integer second order cone programming model that is convenient to handle.

Description

Wind-fire coupling power generation system robust capacity planning method considering distribution network reconfiguration
Technical Field
The invention relates to the field of power systems, in particular to a capacity planning method for a wind-fire coupling power generation system.
Background
The high penetration of renewable resources in large power systems poses a serious challenge to traditional planning and operation due to the randomness of renewable resources. The bundled wind-fire power generation system is called a wind-fire coupled power generation system (WTCGS). Given the uncertainty of wind power itself, WTCGS provides relatively stable power generation for shared transmission lines through optimal power generation scheduling between wind farms and thermal power plants. The flexible combined power generation system mainly adopts a unit commitment (SCUC) subject to safety constraint aiming at a thermal power plant and carries out a load switching strategy through a Distribution Network (DN). This coordination can reduce intermittent high wind fluctuations in the shared transmission line. Different from coordinated scheduling of other power generation modes, when the percentage of wind power on the boundary of the shared power transmission line is slightly increased, the deep peak load regulation pressure of thermal power can be effectively relieved by adopting a load switching strategy. From the operation point of view, the fluctuation of the load demand and the fluctuation of the wind power generation are uncertain factors, so that the establishment of a wind-fire coupled power generation capacity planning model (CPWTCGS) by considering the uncertain factors is important for the WTCGS.
Disclosure of Invention
Aiming at the technical defects, the invention provides a robust capacity planning method of a wind-fire coupled power generation system considering distribution network reconstruction, and solves the technical problem of how to construct a capacity planning model for the wind-fire coupled system by considering distribution network reconstruction and uncertainty factors at the same time.
In order to solve the technical problems, the invention provides a wind-fire coupling power generation system capacity planning method considering distribution network reconstruction and robustness, which comprises the following steps:
step 1: load balancing through power distribution network DN reconstruction and fluctuation of wind power and fire coupling power generation system output power caused by wind power fluctuation through power supply of distributed energy DER are balanced, namely, the wind power and fire coupling power generation system is coordinated to balance fluctuation of total output power through changing a power distribution network topological structure (namely switching on and off states of a circuit breaker) and distributed energy power generation;
establishing a power coordination model: establishing power flow according to power coordination of the wind-fire coupled power generation system and the distribution network reconfiguration, and comprising the following power coordination constraint conditions: wind power active power constraint, distribution network reconfiguration condition constraint, linear active power balance constraint, radial structure constraint, transformer capacity constraint and switching time limit constraint between adjacent time intervals of all circuit breakers;
step 2: establishing a deterministic wind-fire coupling power generation system capacity planning model considering distribution network reconstruction: taking the maximum operation income as an objective function, wherein the objective function comprises the power coordination constraint condition, the thermal power unit output power constraint, the thermal power unit climbing constraint and the circuit breaker switch state value constraint;
and 3, step 3: on the basis of considering a capacity planning model of a deterministic wind-fire coupled power generation system of distribution network reconstruction, based on an elliptic uncertainty theory, considering uncertainty of wind power and load change, and constructing a robust capacity planning model of the wind-fire coupled power generation system considering the distribution network reconstruction;
and 4, step 4: and reconstructing the robust capacity planning model of the wind-fire coupled power generation system which is constructed by considering distribution network reconstruction into a mixed integer second-order conical form.
Further, the power flow is as follows:
power flow:
Figure GDA0003843446200000021
wherein the content of the first and second substances,
Figure GDA0003843446200000022
which represents the total output power at time t,
Figure GDA0003843446200000023
δ% represents the power range specified on the transmission line; p base Representing a reference power on the transmission line;
Figure GDA0003843446200000024
representing the firepower active power on a t-period bus ih;
Figure GDA0003843446200000025
representing wind power active power on a bus ih at the t period;
Figure GDA0003843446200000026
representing the output power of the distributed energy source.
Further, the power coordination constraint is as follows:
wind power active power constraint:
Figure GDA0003843446200000027
wherein v (t) represents the wind speed over a period of t; k is a radical of formula v Representing constants in a wind power generation model; v. of ci Representing a cut-in wind speed; v. of r Representing a rated wind speed;
Figure GDA0003843446200000028
indicating the rated capacity of the wind power generation on the bus ih.
The distribution network reconstruction can be carried out only when one of the following distribution network reconstruction conditions is met:
(i) Order to
Figure GDA0003843446200000029
If it is used
Figure GDA00038434462000000210
And is provided with
Figure GDA00038434462000000211
Then there are:
Figure GDA00038434462000000212
(ii) Order to
Figure GDA00038434462000000213
If it is used
Figure GDA00038434462000000214
And is
Figure GDA00038434462000000215
Then there are:
Figure GDA00038434462000000216
wherein the content of the first and second substances,
Figure GDA00038434462000000217
representing the active load, P, on bus ih at time t Gmin,ih Represents the minimum thermal power on the bus ih, P Gmax,ih Represents the maximum thermal power on the bus ih;
determining a corresponding linear active power balance equation according to the type of the power distribution network, wherein the equation is expressed in a short-hand form as follows:
Figure GDA0003843446200000031
when one of the conditions for the reconfiguration of the distribution network is met, the power balance constraint of the reconfiguration of the distribution network is as follows:
Figure GDA0003843446200000032
wherein the content of the first and second substances,
Figure GDA0003843446200000033
representing the matrix of coefficients relative to the generatrix ih,
Figure GDA0003843446200000034
representing a vector of constants, S, relative to the generatrix ih t A switch state matrix representing the circuit breaker;
determining radial structure constraint according to the type of the power distribution network:
DSC type: s. the i +S j ＝1；
SSC type: s. the i +S j +S k ＝2；
Type TOSSC-3 and TOSSC-2:
Figure GDA0003843446200000035
TDSSC-3 type:
Figure GDA0003843446200000036
TDSSC-2 type: s. the i ＝1；
Wherein S is i 、S j 、S k 、S m 、S n The switch state of each breaker is represented, the value is 0 or 1,1 represents on, and 0 represents off;
and (3) transformer capacity constraint:
Figure GDA0003843446200000037
switching time limits between adjacent time intervals for all circuit breakers:
Figure GDA0003843446200000038
wherein T represents the total number of time segments, TC i,max Represents the rated capacity of substation i, Ω (i) represents a binary variable set of the circuit breaker with respect to substation i;
Figure GDA0003843446200000039
represents the state of the circuit breaker of line k at time t (in binary quantities);
Figure GDA00038434462000000310
representing the total time for reconfiguration of the distribution network up to time t by the circuit breaker of line k
Figure GDA00038434462000000311
To pair
Figure GDA00038434462000000312
Extend to obtain
Figure GDA00038434462000000313
Further, corresponding matrix expressions are determined according to linear active power balance equations under different power distribution network types, and the matrix expressions are respectively as follows:
DSC type:
Figure GDA0003843446200000041
SSC type:
Figure GDA0003843446200000042
type TOSSC-3:
Figure GDA0003843446200000043
type TOSSC-2:
Figure GDA0003843446200000044
TDSSC-3 type:
Figure GDA0003843446200000045
TDSSC-2 type:
Figure GDA0003843446200000046
wherein, P S,A1 、P S,A2 、P S,A3 Representing the actual output power from stations A1, A2, A3, respectively; p is c 、P d 、P e Respectively representing the actual loads on the substations C, D and E; s i 、S j 、S k 、S m 、S n Indicating the switching state of each circuit breaker.
Further, the objective function is as follows:
Figure GDA0003843446200000047
wherein the content of the first and second substances,
Figure GDA0003843446200000048
the power selling income of the wind-fire coupling power generation system in the time period t is as follows:
Figure GDA0003843446200000049
λ S representing the price of electricity sold by the wind-fire coupled power generation system;
Figure GDA00038434462000000410
the method is characterized in that the operation cost of the wind-fire coupling power generation system in the time period t is composed of thermal power output and distribution network reconstruction cost:
Figure GDA00038434462000000411
P B representing electricity prices for purchasing electricity from distribution networks, P H Representing the price of the distribution network reconfiguration service provided by the distribution network; h (S) t ) Representing the total time for reconfiguration of the distribution network up to time t,
Figure GDA0003843446200000051
representing an initial switch state vector for the circuit breaker.
f C The total construction cost of the wind-fire coupling power generation system is as follows:
Figure GDA0003843446200000052
η W representing the total investment cost, eta, of the wind installation G The total investment cost of the thermal power total installation is shown.
Further, the thermal power unit output power constraint, the thermal power unit climbing constraint and the circuit breaker switch state value constraint are respectively as follows:
Figure GDA0003843446200000053
wherein gamma represents the percentage of the minimum output power of the thermal power generating unit to the rated capacity, and delta r Indicating the ramp rate, N, of a thermal power unit y Representing the annual number of planned wind-fire power generation; here S t Representing the switch state vector of the circuit breaker at time t.
Further, step 3 is performed as follows:
step 3.1: and rewriting the deterministic wind-fire coupling power generation system capacity planning model considering distribution network reconstruction into a compact form:
max(α T x-β T S)
s·t.ω T x-ρ≤L·S-D,A T x≤b,S∈K
wherein vectors of decision variables
Figure GDA0003843446200000054
Optimized variables (x, S), S = { S = t }；
K represents a set consisting of the linear active power balance equation, radial structure constraint, transformer capacity constraint, switching time limit constraint between adjacent time intervals of all circuit breakers and circuit breaker switch state value constraint;
Figure GDA0003843446200000055
and omega, L and D are constant coefficient vectors obtained from the thermal power unit output power constraint, the thermal power unit climbing constraint and the circuit breaker switch state value constraint, and contain uncertain wind speed and load requirements; when the distribution network reconstruction condition (i) is satisfied, ρ = (1 + δ%) P base (ii) a When the distribution network reconfiguration condition (ii) is satisfied, ρ = - (1- δ%) P base ；
Figure GDA0003843446200000056
Figure GDA0003843446200000057
A T B is less than or equal to x, and represents thermal power unit power constraint and thermal power unit climbing constraint;
step 3.2: defining an ellipsoid, and converting the compact form of the capacity planning model of the deterministic wind-fire coupled power generation system considering the reconstruction of the distribution network into a robust capacity planning model of the wind-fire coupled power generation system considering the reconstruction of the distribution network;
an ellipsoid: θ · y = ω T x-L T ·S≤ρ-D；
Wherein y = (x, S), θ = [ ω ]) T -L]The coefficient vector θ is affected by random perturbations ω, L, and D;
considering a robust capacity planning model of a wind-fire coupling power generation system for distribution network reconstruction:
Figure GDA0003843446200000061
wherein alpha is n 、β n 、ω n 、L n 、D n Is nominal data, and d α, d β, d ω, dL, dD are zero-mean random perturbations.
Further, step 4 is performed as follows:
setting safety boundaries for d alpha, d beta, d omega, dL and dD in the wind-fire coupled power generation system robust capacity planning model considering distribution network reconstruction, and replacing the safety boundaries with corresponding safety boundaries V α 、V β 、V ω 、V L 、V D The following model was obtained:
Figure GDA0003843446200000062
A T x≤b,S∈K
wherein, V α 、V β 、V ω 、V L Is of the general formula
Figure GDA0003843446200000063
i＝{α,β,ω,L}，
Figure GDA0003843446200000064
Represents the covariance of matrix i; epsilon represents a reasonable deviation value set within an allowable range;
order to
Figure GDA0003843446200000065
Rewriting the model:
Figure GDA0003843446200000066
A T x≤b,S∈K
Figure GDA0003843446200000071
Figure GDA0003843446200000072
will be provided with
Figure GDA0003843446200000073
Reformulating into four SOC constraints to obtain | | | tau i || 2 ≤z i I = α, β, ω, L, thereby constructing the robust capacity planning model of the wind-fire coupled power generation system considering the reconstruction of the distribution network as a mixed integer second-order cone form. Tau is i Is formed by z i Definition of z i And v i Is composed of V i And (4) defining.
Compared with the prior art, the invention has the beneficial effects that:
1) The present invention infers a linear active power balance equation based on radial structure constraints in the actual High Voltage Distribution Network (HVDN) and DN. Therefore, a deterministic capacity planning model (CPWTCGS) of the wind-fire coupled power generation system is formed, i.e., a deterministic capacity planning model of the wind-fire coupled power generation system considering the reconfiguration of the distribution network.
2) The robust CPWTCGS method is used for constructing the robust CPWTCGS, namely a wind-fire coupling power generation system robust capacity planning model considering distribution network reconstruction, and reconstructing the model into a mixed integer second-order cone (MISOCP) form. In addition, it can handle SOC relaxation issues, which can ultimately be solved by commercial software such as MOSEK and barron.
Drawings
FIG. 1 is a network structure diagram of coordinated output power of a wind-fire coupled power generation system and a power distribution network;
fig. 2 is a schematic structural view of a DSC type distribution network;
fig. 3 is a schematic diagram of the structure of an SSC type power distribution network;
FIG. 4 is a schematic diagram of a TOSSC-3 type distribution network;
FIG. 5 is a schematic diagram of a TOSSC-2 type distribution network;
FIG. 6 is a schematic diagram of a TDSC-3 type distribution network;
fig. 7 is a schematic structural diagram of a TDSSC-2 type power distribution network.
Detailed Description
Establishing a power coordination model
First, the power flow of WTCGS is depicted. Wind power and thermal power can be bound together, the total output power of the wind power and thermal power can be coordinated with a reconfigurable DN and a reconfigurable DER at the same time, as shown in a figure 1, the load is balanced through the reconfiguration of a distribution network DN, and the fluctuation of the output power of a wind-thermal coupling power generation system caused by the fluctuation of wind power is balanced through the power supply of distributed energy DER, namely, the fluctuation of the total output power is balanced through changing the topological structure of a distribution network (namely switching the on-off state of a circuit breaker) and coordinating the wind-thermal coupling power generation system through the power generation of the distributed energy; thus, they are clustered at a location where relatively stable power fluctuations can be provided over a specified range by the transmission line.
Establishing a power coordination model: establishing power flow according to power coordination of the wind-fire coupling power generation system and distribution network reconfiguration, wherein the power flow comprises the following power coordination constraint conditions: wind power active power constraint, distribution network reconfiguration condition constraint, linear active power balance constraint, radial structure constraint, transformer capacity constraint and switching time limit constraint between adjacent time intervals of all circuit breakers;
Figure GDA0003843446200000081
Figure GDA0003843446200000082
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003843446200000083
and the power output of the wind turbine generator with a given v (t). Assignable load (or active power support provided by DER) is a necessary condition to achieve equality. The network reconfiguration conditions for WTCGS are as follows:
Figure GDA0003843446200000084
Figure GDA0003843446200000085
active power balance and radial structure constraints
Since WTCGS has been widely used in power transmission systems, the load switching operation of WTCGS is imposed on HVDN (the power distribution network in the present invention is a high voltage power distribution network HVDN), such as 110kV HVDN [30] in china. HVDNs differ from Low Voltage Distribution Networks (LVDNs), which generally have a shallow and wide structure. Fig. 2-7 show six typical 110kV HVDN type networks. They are labeled direct power connection (DSC), serial power connection (SSC), T-type with one serial power connection of three sites (TOSSC-3), T-type with one serial power connection of two sites (TOSSC) -2), T-type with dual serial power connection of three sites (TDSSC-3) and T-type with dual serial power connection of two sites (TDSSC-2), respectively. The symbols in equations (3) - (8) are labeled in fig. 2-7, respectively.
For simplicity, the applicant developed simplified power balancing equations for each typical 110kV HVDN type network. They are formulated as linear matrix equations. As shown in fig. 2 and 3, the simplified active power balance equation can be expressed in the form of a matrix, thereby deriving
Figure GDA0003843446200000091
Figure GDA0003843446200000092
By replacing the box enclosed by dashed lines in fig. 4 with a virtual workstation A4 for TOSSC-3, applicants have found that it is equivalent to an SSC,
Figure GDA0003843446200000093
for TOSSC-2, the graph in FIG. 4 may be added to TOSSC-3, and (4) re-expressed as TOSSC-3
Figure GDA0003843446200000094
For TDSSC-3, the general assumption is that for each typical type of cell in an HVDN, one station supplies up to two substations; otherwise, the branch may overheat when the substation is in a full load condition. Thus, at most, station A2 only changes directionThe stations C and D provide power as shown in fig. 6. In other words, S j ，S k And S n Cannot be opened simultaneously. Likewise, the A3 station powers the C and E stations with maximum capacity. Thus S j ，S m And S n Cannot be simultaneously 1. In this case, there are
Figure GDA0003843446200000095
For TDSSC-2, applicants have found that this can be added from fig. 6. However, when power is supplied from the slave station A2 to the three slave stations, si should be turned off all the time. Therefore, re-expression (6) as
Figure GDA0003843446200000096
The compact forms of (3) - (7) can be re-expressed as
Figure GDA0003843446200000101
Wherein
Figure GDA0003843446200000102
And
Figure GDA0003843446200000103
representation relative to bus i h A coefficient matrix and a constant vector. When one of (2) is activated, it may represent the power required by WTCGS for hour t. Thus, there are
Figure GDA0003843446200000104
In addition, fig. 2 shows that the radial structural constraint consists of a linear equation and an inequality.
DSC:S i +S j ＝1. (9a)
SSC:S i +S j +S k ＝2. (9b)
TOSSC-3and TOSSC-2:
Figure GDA0003843446200000105
TDSSC-3:
Figure GDA0003843446200000106
(9d) The radial structural constraint of TDSSC-2 is given in (1), si =1.
Deterministic CPWTCGS model
In addition to the active power equality and radial structure constraints mentioned in section B, CPWTCGS also includes transformer capacity constraints in sites with binary St, and switching time limits between adjacent time intervals for all circuit breakers. To eliminate heavy-duty or overloaded transformers during peak hours, transformer capacity limitation is considered as a strict condition limiting the combined solution shown in (10 a). For limited switching operations, (10 b) may be used to smooth the load drop of the DN.
Figure GDA0003843446200000107
Figure GDA0003843446200000108
Wherein H (S) t ) Representing the total switching time t for a network reconfiguration in hours t. For
Figure GDA0003843446200000109
Extension H (S) t ) Result in
Figure GDA00038434462000001010
In the deterministic formula, the wind turbine power and load requirements are known. For the CPWTCGS problem, the objective function can be viewed as two parts: the construction cost of the WTCGS and the life cycle operation cost of the WTCGS at a fixed capacity. The applicant makes the following assumptions. (i) All operating costs are 10 years old and it is assumed that the annual costs are almostAre equal. (ii) For simplicity, all thermal power generating units in WTCGS are kept on all the time, which means that the model of the present invention contains the optimal power flow, but does not include the SCUC. This assumption is primarily intended to discuss the importance of DN reconfiguration in CPWTCGS, as well as reducing the complexity of CPWTCGS. (iii) As described above, the entire DN of WTCGS is divided by only six typical types. Conceptually, the vector of decision variables is
Figure GDA00038434462000001011
And the vector of the manipulated variable is
Figure GDA00038434462000001012
Therefore, the deterministic CPWTCGS model for 8760 hours a year can be written as (11 a), which consists of the annual operating costs
Figure GDA0003843446200000111
And annual average construction cost Fc/Ny
Figure GDA0003843446200000112
Figure GDA0003843446200000113
(11b) The last two inequalities in the process are thermal power unit power constraint and thermal power unit climbing constraint respectively; f. of B Means that the WTCGS can sell the electricity at t hours
Figure GDA0003843446200000114
f C Is the total building cost, defined as
Figure GDA0003843446200000115
f o Is the operation cost of any t, consists of thermal power generation and network reconfiguration cost, and has the calculation formula as follows:
Figure GDA0003843446200000116
wherein the content of the first and second substances,
Figure GDA0003843446200000117
refers to the thermal power generation cost that can be approximated by a piecewise linear function; the second term represents the purchase cost or absolute value up to (8); the last item is the conversion service cost defined in (10 b).
Restructured as a mixed integer second order tapered form
Robust Optimization (RO) is a useful tool for decision-making under uncertainty that can protect the system from worst-case effects, but is conservative due to the low probability of extreme events. RO can be used for CPWTCGS problems, whose load demand and wind parameters are unknown. For convenience, applicants define vectors of decision variables as
Figure GDA0003843446200000118
Figure GDA0003843446200000119
And (10) can be rewritten as a Mixed Integer Linear Programming (MILP) model, where the optimization variable is
Figure GDA00038434462000001110
The compact form of CPWTCGS is redefined as
maxα T x-β T S. (13a)
s.t.ω T x-ρ≤L·S-D,A T x is less than or equal to b, and S is equal to K (13 b) in which
Figure GDA00038434462000001111
And ω, L and D are constant coefficient vectors obtained from (11 b) containing uncertain wind speed and load demand.
α T x means
Figure GDA00038434462000001112
And beta T S represents
Figure GDA00038434462000001113
When (2 a) holds ρ = (1 + δ%) P base Or (2 b) when it is true ρ = - (1- δ%) P base ；A T x ≦ b represents the boundary and slope constraint in (11 b).
Applicants naturally assume that the different uncertain perturbation factors affecting ω and L are random and independent of each other. Defining the ellipsoid of (13 b) as θ · y = ω T x-L T S ≦ ρ -D where y = (x, S), θ = [ ω [ ] T -L]The coefficient vector θ is affected by a random perturbation ω, land d. Let
Figure GDA00038434462000001114
Wherein theta is n andD n And vectors that are nominal coefficients, respectively;
Figure GDA00038434462000001115
and
Figure GDA00038434462000001116
are respectively provided with a zero mean and a covariance matrix V θ andV D The random perturbation vector of (2). Recall that the value of the random variable never exceeds the product θ · y + D of its mean value plus the standard deviation, thus concluding:
Figure GDA0003843446200000121
when y satisfies (14), the random disturbance can be limited from above
Figure GDA0003843446200000122
The probability that the vector of (b) violates the constraint θ y + D ≦ ρ evaluated at y, when the normal distribution is satisfied, the Tschebyshev chebyshev inequality range may be increased to
Figure GDA0003843446200000123
To ensure that the limit in (15) is less than the negative sextic order of magnitude of 10 (≦ 10) -6 ) ε is set to 5.13. Therefore, the robust CPWTCGS model with the following elliptical model with uncertainty
Figure GDA0003843446200000124
Figure GDA0003843446200000125
Wherein (alpha) n ,β n ,ω n ,L n ,D n ) Is nominal data, while the sum d α, d β, d ω, dLand dD is a zero-mean random perturbation.
To determine that each of the random perturbations d α, d β, d ω, dLand dD satisfies the assumption in (15), let V α ,V β ,V ω ,V L andV D Is the corresponding upper bound of the perturbation covariance matrix. One security parameter is selected and all constrained objects and subjects are replaced with their security boundaries as described above. The following optimization problem results.
Figure GDA0003843446200000126
Figure GDA0003843446200000127
Figure GDA0003843446200000128
A T x is less than or equal to b, S is equal to K (17 d)
Figure GDA0003843446200000129
Wherein i = α, β, ω, land v i Can be composed of V i Is given such that
Figure GDA00038434462000001210
Figure GDA00038434462000001211
In particular to
Figure GDA00038434462000001212
s.t.((α n ) T x+ε·z α )-((β n ) T S+ε·z β )≥μ. (18b)
Figure GDA00038434462000001213
A T x≤b,S∈K. (18d)
Figure GDA00038434462000001214
Figure GDA00038434462000001215
Obviously, (18 a) is a linear target and (18 b) - (18 e) are the full linear inequalities and equalities. However, it is possible to use a single-layer,
(18f) Is a quadratic equation. Therefore, an attempt is made to reformulate (18 f) into four SOC constraints, resulting in
||τ i || 2 ≤z i , i＝α,β,ω,L. (19)
Observations of (18 a) - (18 e) and (19) show that the CPWTCGS model has an ellipsoidal uncertainty, conforming to the Mixed Integer Second Order Cone (MISOCP) form.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. A robust capacity planning method for a wind-fire coupled power generation system considering distribution network reconfiguration is characterized by comprising the following steps:
step 1: load balancing is achieved through power distribution network DN reconstruction, and fluctuation of wind power and fire power coupling power generation system output power caused by wind power fluctuation is balanced through power supply of distributed energy DER, namely the wind power and fire coupling power generation system is coordinated through changing of a power distribution network topological structure and distributed energy power generation to balance fluctuation of the total output power;
establishing a power coordination model: establishing power flow according to power coordination of the wind-fire coupling power generation system and distribution network reconfiguration, wherein the power flow comprises the following power coordination constraint conditions: wind power active power constraint, distribution network reconfiguration condition constraint, linear active power balance constraint, radial structure constraint, transformer capacity constraint and switching time limit constraint between adjacent time intervals of all circuit breakers;
and 2, step: establishing a deterministic wind-fire coupling power generation system capacity planning model considering distribution network reconstruction: taking the maximum operation income as an objective function, wherein the objective function comprises the power coordination constraint condition, the thermal power unit output power constraint, the thermal power unit climbing constraint and the circuit breaker switch state value constraint;
and 3, step 3: on the basis of considering a capacity planning model of a deterministic wind-fire coupled power generation system of distribution network reconstruction, based on an elliptic uncertainty theory, considering uncertainty of wind power and load change, and constructing a robust capacity planning model of the wind-fire coupled power generation system considering the distribution network reconstruction;
and 4, step 4: and reconstructing the robust capacity planning model of the wind-fire coupling power generation system, which is constructed by considering the reconstruction of the distribution network, into a mixed integer second-order conical form.
2. The robust capacity planning method for wind-fire coupled power generation system considering distribution network reconfiguration according to claim 1, characterized in that the power flows are as follows:
power flow:
Figure FDA0003849835030000011
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003849835030000012
which represents the total output power at time t,
Figure FDA0003849835030000013
δ% represents the power range specified on the transmission line; p is base Representing a reference power on the transmission line;
Figure FDA0003849835030000014
representing the firepower active power on a t-period bus ih;
Figure FDA0003849835030000015
representing wind power active power on a bus ih in a time period t;
Figure FDA0003849835030000016
represents the output power of the distributed energy source;
Figure FDA0003849835030000017
and the active load on the bus ih in the period t is shown.
3. The robust capacity planning method for the wind-fire coupled power generation system considering the distribution network reconfiguration is characterized in that the power coordination constraint is as follows:
wind power active power constraint:
Figure FDA0003849835030000021
whereinV (t) represents the wind speed over a period of t; k is a radical of formula v Representing constants in a wind power generation model; v. of ci Representing a cut-in wind speed; v. of r Represents a rated wind speed;
Figure FDA0003849835030000022
represents the rated capacity of wind power generation on the bus ih;
the distribution network reconfiguration can be carried out only when one of the following distribution network reconfiguration conditions is satisfied:
(i) Order to
Figure FDA0003849835030000023
If it is used
Figure FDA0003849835030000024
And is provided with
Figure FDA0003849835030000025
Then there are:
Figure FDA0003849835030000026
(ii) Order to
Figure FDA0003849835030000027
If it is not
Figure FDA0003849835030000028
And is provided with
Figure FDA0003849835030000029
Then there are:
Figure FDA00038498350300000210
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00038498350300000211
representing the active load, P, on bus ih at time t Gmin,ih Represents the minimum thermal power on the bus ih, P Gmax,ih Represents the maximum thermal power on the bus ih;
determining a corresponding linear active power balance equation according to the type of the power distribution network, wherein the equation is expressed in a short-hand form as follows:
Figure FDA00038498350300000212
when one of the conditions of the distribution network reconfiguration is met, the power balance constraint of the distribution network reconfiguration is as follows:
Figure FDA00038498350300000213
wherein the content of the first and second substances,
Figure FDA00038498350300000214
representing the matrix of coefficients relative to the generatrix ih,
Figure FDA00038498350300000215
representing a vector of constants, S, relative to the generatrix ih t A switch state matrix representing the circuit breaker;
determining radial structure constraint according to the type of the power distribution network:
DSC type: s i +S j ＝1；
SSC type: s i +S j +S k ＝2；
Type TOSSC-3 and TOSSC-2:
Figure FDA00038498350300000216
TDSSC-3 type:
Figure FDA00038498350300000217
TDSSC-2 type: s i ＝1；
Wherein S is i 、S j 、S k 、S m 、S n The switch state of each breaker is represented, the value is 0 or 1,1 represents on, and 0 represents off;
and (3) transformer capacity constraint:
Figure FDA0003849835030000031
switching time limits between adjacent time intervals for all circuit breakers:
Figure FDA0003849835030000032
wherein T represents the total number of time segments, TC i,max Representing the rated capacity of substation i, Ω (i) representing a binary variable set of the circuit breaker with respect to substation i;
Figure FDA0003849835030000033
represents the state of the circuit breaker of line k at time t;
Figure FDA0003849835030000034
representing the total time for reconfiguration of the distribution network up to time t by the circuit breaker of line k
Figure FDA0003849835030000035
To pair
Figure FDA0003849835030000036
Extend to obtain
Figure FDA0003849835030000037
Wherein, delta s A switching time limit value representing the opening of the circuit breaker;
Figure FDA0003849835030000038
indicating initial opening of circuit breakerAn off-state vector.
4. The robust capacity planning method for wind-fire coupled power generation system considering distribution network reconfiguration according to claim 3, characterized in that the corresponding matrix expressions are determined according to the linear active power balance equations under different distribution network types, as follows:
DSC type:
Figure FDA0003849835030000039
SSC type:
Figure FDA00038498350300000310
type TOSSC-3:
Figure FDA00038498350300000311
type TOSSC-2:
Figure FDA00038498350300000312
TDSSC-3 type:
Figure FDA00038498350300000313
TDSSC-2 type:
Figure FDA0003849835030000041
wherein, P S,A1 、P S,A2 、P S,A3 Representing the actual output power from stations A1, A2, A3, respectively; p c 、P d 、P e Respectively representing the actual loads on the substations C, D and E; s i 、S j 、S k 、S m 、S n Indicating the switching state of each circuit breaker.
5. The robust capacity planning method for wind-fire coupled power generation system considering distribution network reconfiguration according to claim 2, wherein the objective function is as follows:
Figure FDA0003849835030000042
wherein the content of the first and second substances,
Figure FDA0003849835030000043
the power selling income of the wind-fire coupling power generation system in the time period t is as follows:
Figure FDA0003849835030000044
λ S representing the price of electricity sold by the wind-fire coupled power generation system;
Figure FDA0003849835030000045
the method is characterized in that the operation cost of the wind-fire coupling power generation system in the time period t is composed of thermal power output and distribution network reconstruction cost:
Figure FDA0003849835030000046
P B representing electricity prices for purchasing electricity from distribution networks, P H Representing the price of the distribution network reconfiguration service provided by the distribution network; h (S) t ) Representing the total time for reconfiguration of the distribution network up to time t,
Figure FDA0003849835030000047
Figure FDA0003849835030000048
an initial switch state vector representing the circuit breaker;
f C the total construction cost of the wind-fire coupled power generation system is as follows:
Figure FDA0003849835030000049
η W representing the total investment cost, eta, of the wind installation G The total investment cost of the thermal power general installation is represented;
Figure FDA00038498350300000410
representing the active load on a bus ih at the moment t; s t A switch state matrix representing the breaker at time t; n is a radical of y Indicating the annual number of parts planned to generate electricity using wind and fire.
6. The robust capacity planning method considering the wind-fire coupled power generation system with the distribution network reconfiguration is characterized in that the output power constraint of the thermal power unit, the climbing constraint of the thermal power unit and the switch state value constraint of the circuit breaker are respectively as follows:
Figure FDA00038498350300000411
wherein gamma represents the percentage of the minimum output power of the thermal power generating unit to the rated capacity, and delta r Indicating the ramp rate, N, of a thermal power unit y Representing the annual number of planned wind-fire power generation; here S t Representing the switch state vector of the breaker at time t.
7. The robust capacity planning method for the wind-fire coupled power generation system considering the distribution network reconfiguration is characterized in that the step 3 is carried out as follows:
step 3.1: and rewriting the deterministic wind-fire coupling power generation system capacity planning model considering distribution network reconstruction into a compact form:
max(α T x-β T S)
s·t.ω T x-ρ≤L·S-D,A T x≤b,S∈K
wherein vectors of decision variables
Figure FDA0003849835030000051
Optimized variables (x, S), S = { S = t }；S t A switching state vector representing the breaker at time t;
k represents a set consisting of the linear active power balance equation, radial structure constraint, transformer capacity constraint, switching time limit constraint between adjacent time intervals of all circuit breakers and circuit breaker switch state value constraint;
Figure FDA0003849835030000052
and omega, L and D are constant coefficient vectors obtained from the thermal power unit output power constraint, the thermal power unit climbing constraint and the circuit breaker switch state value constraint, and contain uncertain wind speed and load requirements; when the distribution network reconfiguration condition (i) is satisfied, ρ = (1 + δ%) P base (ii) a When the distribution network reconfiguration condition (ii) is satisfied, ρ = - (1- δ%) P base ；
Figure FDA0003849835030000053
Wherein, the distribution network reconfiguration conditions (i) and (ii) are respectively as follows:
(i) Order to
Figure FDA0003849835030000054
If it is not
Figure FDA0003849835030000055
And is provided with
Figure FDA0003849835030000056
Then there are:
Figure FDA0003849835030000057
(ii) Order to
Figure FDA0003849835030000058
If it is used
Figure FDA0003849835030000059
And is
Figure FDA00038498350300000510
Then there are:
Figure FDA00038498350300000511
Figure FDA00038498350300000512
A T x is less than or equal to b, representing the power constraint of the thermal power unit and the climbing constraint of the thermal power unit;
step 3.2: defining an ellipsoid, and converting the compact form of the capacity planning model of the deterministic wind-fire coupled power generation system considering the reconstruction of the distribution network into a robust capacity planning model of the wind-fire coupled power generation system considering the reconstruction of the distribution network;
an ellipsoid: θ · y = ω T x-L T ·S≤ρ-D；
Wherein y = (x, S), θ = [ ω ]) T -L]The coefficient vector θ is affected by random perturbations ω, L, and D;
considering a robust capacity planning model of a wind-fire coupling power generation system for distribution network reconstruction:
Figure FDA00038498350300000513
Figure FDA0003849835030000061
wherein alpha is n 、β n 、ω n 、L n 、D n Is nominal data, and d α, d β, d ω, dL, dD are zero-mean random perturbations.
8. The robust capacity planning method for the wind-fire coupled power generation system considering the distribution network reconfiguration is characterized in that the step 4 is carried out as follows:
for the robust capacity gauge of the wind-fire coupling power generation system considering distribution network reconstructionSetting safety boundaries by d alpha, d beta, d omega, dL and dD in the stroke model and replacing the safety boundaries by corresponding safety boundaries V α 、V β 、V ω 、V L 、V D The following model was obtained:
Figure FDA0003849835030000062
Figure FDA0003849835030000063
Figure FDA0003849835030000064
A T x≤b,S∈K
wherein d alpha, d beta, d omega, dL and dD are random disturbance with zero mean value; v α 、V β 、V ω 、V L Is of the general formula
Figure FDA0003849835030000065
Figure FDA0003849835030000066
Represents the covariance of matrix i; epsilon represents a reasonable deviation value set within an allowable range;
order to
Figure FDA0003849835030000067
Rewriting the model:
Figure FDA0003849835030000068
s.t.((α n ) T x+ε·z α )-((β n ) T )S+ε·z β ≥μ
Figure FDA0003849835030000069
A T x≤b,S∈K
Figure FDA00038498350300000610
Figure FDA00038498350300000611
will be provided with
Figure FDA00038498350300000612
Reformulation is four SOC constraints to obtain | | tau i || 2 ≤z i I = α, β, ω, L, thereby constructing the robust capacity planning model of the wind-fire coupled power generation system considering the reconstruction of the distribution network as a mixed integer second-order cone form.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105391061A (en) * 2015-12-01 2016-03-09 贵州电网有限责任公司 Active power distribution network optimization system and optimization method of cooling, heating and power wind-photovoltaic storage joint optimization
CN107979092A (en) * 2017-12-18 2018-05-01 国网宁夏电力有限公司经济技术研究院 It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access
CN108321804A (en) * 2018-02-26 2018-07-24 清华大学 Electric system dual-layer optimization operation method containing battery energy storage and integrated wind plant
CN108832665A (en) * 2018-07-04 2018-11-16 四川大学 A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation
CN109274134A (en) * 2018-11-08 2019-01-25 东南大学 A kind of active distribution network robust active reactive coordination optimizing method based on time series scene analysis
CN110797919A (en) * 2019-12-05 2020-02-14 国网四川省电力公司经济技术研究院 Clean energy power supply planning method based on Wasserstein distance and distribution robust optimization
CN111900732A (en) * 2020-08-04 2020-11-06 四川大学 Wind power cluster access mode planning method considering wind power uncertainty and reactive power flow and voltage constraints
CN111952966A (en) * 2019-12-11 2020-11-17 国网辽宁省电力有限公司经济技术研究院 Source load storage system staged coordination optimization method considering wind power consumption capacity
CN112072641A (en) * 2020-08-19 2020-12-11 国网江苏省电力有限公司扬州供电分公司 Source network load storage flexible coordination control and operation optimization method

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108599154B (en) * 2018-05-14 2021-05-11 东南大学 Three-phase unbalanced distribution network robust dynamic reconstruction method considering uncertainty budget
CN110322051A (en) * 2019-06-06 2019-10-11 国网浙江省电力有限公司经济技术研究院 Consider the integrated energy system Optimal Configuration Method of N-1 security constraint
CN110829502B (en) * 2019-10-17 2022-06-21 广西电网有限责任公司电力科学研究院 Multi-target interval power generation scheduling method considering new energy
CN110969284B (en) * 2019-10-29 2022-09-09 国网河南省电力公司经济技术研究院 Double-layer optimized scheduling method for power distribution network

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105391061A (en) * 2015-12-01 2016-03-09 贵州电网有限责任公司 Active power distribution network optimization system and optimization method of cooling, heating and power wind-photovoltaic storage joint optimization
CN107979092A (en) * 2017-12-18 2018-05-01 国网宁夏电力有限公司经济技术研究院 It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access
CN108321804A (en) * 2018-02-26 2018-07-24 清华大学 Electric system dual-layer optimization operation method containing battery energy storage and integrated wind plant
CN108832665A (en) * 2018-07-04 2018-11-16 四川大学 A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation
CN109274134A (en) * 2018-11-08 2019-01-25 东南大学 A kind of active distribution network robust active reactive coordination optimizing method based on time series scene analysis
CN110797919A (en) * 2019-12-05 2020-02-14 国网四川省电力公司经济技术研究院 Clean energy power supply planning method based on Wasserstein distance and distribution robust optimization
CN111952966A (en) * 2019-12-11 2020-11-17 国网辽宁省电力有限公司经济技术研究院 Source load storage system staged coordination optimization method considering wind power consumption capacity
CN111900732A (en) * 2020-08-04 2020-11-06 四川大学 Wind power cluster access mode planning method considering wind power uncertainty and reactive power flow and voltage constraints
CN112072641A (en) * 2020-08-19 2020-12-11 国网江苏省电力有限公司扬州供电分公司 Source network load storage flexible coordination control and operation optimization method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Optimal strategies for distribution network reconfiguration considering uncertain wind power;Zhiwei Cui等;《 CSEE Journal of Power and Energy Systems ( Volume: 6, Issue: 3, September 2020)》;20200406;662-671页 *
Unbalanced Voltage/Power Control in Bipolar DC Distribution Grids Using Power Flow Controller;Jianquan Liao等;《2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)》;20200730;1290-1295页 *
考虑合环电流约束的主动配电网转供优化模型;周念成等;《电工技术学报》;20200831;第35卷(第15期);3281-3289页 *

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